Non-invasive imaging technologies allow images of the internal structures of a patient or object to be obtained without performing an invasive procedure on the patient or object. In particular, technologies such as computed tomography (CT) use various physical principals, such as the differential transmission of x-rays through the target volume, to acquire image data and to construct tomographic images (e.g., three-dimensional representations of the interior of the human body or of other imaged structures).
However, image reconstruction algorithms generally assume that the subject of the scan is stationary throughout the data acquisition. Thus, if the patient or object moves during data acquisition, motion artifacts may arise in the tomographic images, or image reconstructions. Such artifacts can lead to confusion for a physician or patient reviewing the reconstructed image.
Known approaches to motion correction typically determine a motion path and attempt to compensate for motion during reconstruction or post-reconstruction based on the motion path. However, such approaches come with the cost of significant algorithmic complexity and computational expense, as well as substantially increased reconstruction time. It may therefore be desirable to develop an efficient technique to improve image quality of acquired CT images by reducing motion artifacts.